iiinova — where your digital self keeps evolving

This is a preprint, so any feedback and critiques are very welcome. Many thanks to my amazing co-authors @robertboehm.bsky.social , Armin Granulo, and Christoph Fuchs!

000

9/ Overall, our findings suggest that giving citizens a meaningful role in democratic policymaking may help reduce resistance and facilitate the implementation of important—but contested—system-level policies.

100

8/ The effects were robust across different policy domains and did not depend strongly on: • how important the restricted behavior was to participants, or • whether participants supported or opposed the policy.

100

7/ Why does participation matter? Our results suggest two mechanisms: • It makes the decision-making process seem fairer (procedural fairness). • It signals that others support the policy (descriptive social norms).

100

6/ Main finding: ✅ Both referenda and citizen forums reduced resistance to system-level policies. Even more interesting, this effect extended to people who didn't participate themselves—simply knowing that other citizens had participated reduced resistance.

100

5/ We combined: 📍 A field case study of US sugar-sweetened beverage taxes 🧪 Seven preregistered experiments 👥 8,083 participants to test whether public participation reduces resistance to system-level policies.

100

4/ We asked whether public participation can help. Specifically, we examined two common forms: 🗳️ Referenda — citizens vote on a policy. 💬 Citizen forums — citizens discuss the policy and express their views before a decision is made.

100

3/ One important reason is psychological reactance. When people perceive a policy as threatening their freedom, they often respond with anger and opposition. This creates a dilemma: the policies that may be most effective are often the hardest to enact politically.

2/ Addressing challenges like climate change and pandemics requires large-scale behavior change. System-level policies (e.g., taxes, bans) are often more effective than individual-level interventions—but they also tend to provoke strong public resistance, especially before they are implemented.

100

Qinyu Xiao @qinyuxiao.bsky.social · 31/05/2026

1/ 🚨 New preprint 🚨 How can governments implement effective—but often unpopular—policies without triggering public backlash? We investigated whether involving citizens in policymaking can reduce resistance to system-level policies. 🧵 Preprint: lnkd.in/dQGy47u4

173

Reposted by Lenni

New working paper with @robertboehm.bsky.social: it is an early version likely needing significant revision, so feedback is very much appreciated. We review multi-level games: a family of economic games that capture tensions within and between groups simultaneously. (1/5) doi.org/10.31234/osf...

doi.org

OSF

11111

Reposted by Lenni

Robert Böhm @robertboehm.bsky.social · 30/04/2026

🚨 New in @science.org: CDC communication undermines trust in vaccines Link to the publication: www.science.org/doi/10.1126/... I’m excited to share this paper, which I wrote together with @schneali.bsky.social, @corneliabetsch.bsky.social & @laulilleholt.bsky.social (1/5)

14118

Reposted by Lenni

Max kuhn @topepo.bsky.social · 31/03/2026

We've released the first version of our tabpfn #rstats package to CRAN. This is an interface to the Python #TabPFN package. tidyverse.org/blog/2026/03... The model is a pre-trained deep learning model that has performed exceedingly well on every data set I've tested it on.

tidyverse.org

tabpfn 0.1.0

A new R package for tabular deep learning models.

1306

Reposted by Lenni

Dan Quintana @dsquintana.bsky.social · 07/03/2026

I’m reminded of this comic almost every time I check LinkedIn

81325264

Many thanks to my amazing co-authors and supervisors @robertboehm.bsky.social and Christoph Fuchs for their work and ongoing support <3

000

💡 The bigger picture: Workers’ economic expectations of AI vary across countries and are shaped by socio-economic development and culture. This suggests that organizations and policymakers should tailor AI strategies to national contexts rather than rely on one-size-fits-all approaches

100

In the paper, we discuss potential reasons why this may be the case and report extensive robustness checks, including alternative country-level indicators and different model specifications, across which the main results remain stable.

100

🔍 Key findings: - Workers in countries with higher levels of socio-economic development tend to report less positive economic expectations of AI. - Culture matters: Workers in culturally tighter societies report more positive economic expectations of AI than workers in looser societies.

100

We find that workers hold positive economic expectations of AI, but there is substantial cross-country variation. In some countries, expectations are more positive, while in others they are closer to neutral or skeptical. To understand these differences, we explore country-level characteristics.

100

In this paper, we analyze survey data from 14,600+ workers across 31 countries to examine how workers expect AI to affect the economy, the labor market, and their own jobs, and how these expectations of AI’s economic impact vary across countries.

Ian Hussey @ianhussey.mmmdata.io · 18/12/2025

🚀 My first PhD paper is out! I'm glad to share my first PhD publication: "Country-level differences in socio-economic development and cultural dimensions are associated with workers' economic expectations of AI", published in Computers in Human Behavior Reports (OA: doi.org/10.1016/j.ch...).

1111

Reposted by Lenni

Oliver Sacks admitted his case studies in The Man Who Mistook His Wife For A Hat were fraudulent “fairy tales”. What Psych101 core texts are left? www.newyorker.com/magazine/202...

76318

Reposted by Lenni

Health Psychology Review @healthpsychrev.bsky.social · 26/11/2025

Emphasizing herd immunity might be a helpful strategy to improve vaccine uptake 💉 In this meta-analysis, authors found small positive, but heterogenous effects. Interestingly, effects are twice as large for experiential methods, such as VR or simulations! Read the full paper here: doi.org/qc4j

Screenshot of the first page of the article, including title "Emphasising herd immunity in vaccine advocacy: a systematic review and meta-analysis", authors and abstract.

011

Reposted by Lenni

Robert Böhm @robertboehm.bsky.social · 17/11/2025

Tomorrow marks the start of 🌍 World AMR Awareness Week 2025! We’ll be sharing insights from our AMR research as psychologists at @univie.ac.at @ipb.bsky.social @unierfurt.bsky.social and BNITM. Like/share this post for updates throughout the week! #AMR #WorldAMRAwarenessWeek #WAAW2025

22212

Reposted by Lenni

Crystal Lewis @cghlewis.bsky.social · 07/11/2025

Stop being a lame-o and replace dplyr with the more slappin genzplyr.

06212

Reposted by Lenni

Robert Böhm @robertboehm.bsky.social · 29/10/2025

We're hiring! @univie.ac.at is seeking a TT Assistant Professor in the Psychology of Digitalization. If your work is about automation, AI, or immersive technology (e.g., VR) in the context of work and organizations (broadly defined), we’d love to hear from you. 👇

24637

Reposted by Lenni

Ryan Briggs @ryancbriggs.net · 24/10/2025

I think this is kind of neat and I don't think anyone else has noticed it (I've looked and I can't find anyone who has) osf.io/preprints/so... Maybe I should back off "justification" language, but it's at least a remarkable coincidence. I still think someone else *must* have noticed it...

Title: A Justification for 80% Power
Abstract:
Cohen’s heuristic reason for choosing 80% power (balancing Type I and TypeII errors) conveniently arrives at approximately the same number as an approachwhere one maximizes the marginal gain in power per standard error reduction. Ihave yet to see someone point this out, and this is interesting because it providesa non-arbitrary justification for 80% power.

56921

Reposted by Lenni

Robert Böhm @robertboehm.bsky.social · 25/09/2025

My PhD student @qinyuxiao.bsky.social has written a wonderful tribute to Gary Bornstein’s influential paper on team games (doi.org/10.1207/S153...) — a paper that remains as relevant today as it was over 20 years ago. You can read Qinyu’s short piece here: doi.org/10.1038/s441...

doi.org

The multi-level social dilemmas of intergroup interactions - Nature Reviews Psychology

Nature Reviews Psychology - The multi-level social dilemmas of intergroup interactions

096

Indeed, your article was by far the easiest to code 🙂. That really reflects the clarity and transparency of your reporting—much appreciated!

Thank you for all the help and guidance! I couldn't ask for a better mentor 🫶

000

The article is open access 🔓, preregistered 📑, and fully transparent: we share all data, effect size computations, screenshots of extracted effects, and full analysis code. 🧑‍💻📂 Just see the post above. That's all! End of thread! Thanks for your attention <3

000

Personal note: this is the most work I’ve ever put into a project (yet). I learned so much, and I’m grateful to see it published. With vaccine hesitancy rising, it’s important to remember that every (non-)vaccination also has a social impact

100

Takeaway 💡 Communicating herd immunity has a small but positive effect on vaccination motivation. However, the effect is larger when people can experience it (VR, simulations) instead of just reading text. 🚀 As such, future messages must be clear, engaging & immersive 🕶️💉

100

We also ran some fancy machine learning 🧠, multiple tests for publication bias (spoiler: none found ✅), assessed Risk of Bias (all good), and packed in plenty of other cool stuff (including a HUGE table 📊). For all the details—you’ll have to check out the paper 😉

Among the most relevant moderators, we identify the medium of communication. When people can actually experience herd immunity (e.g., VR, simulations), the effect on vaccination motivation more than doubles (g = 0.29) 🚀

100

📊 Herd immunity messages do boost vaccination motivation! We find a small but positive summary effect: g = 0.12 (95% CI: 0.08–0.17) 📈 But averages only tell part of the story—what’s even more interesting is when and how these messages work. That’s what we tested next.

100

To find out, we ran the first systematic review & meta-analysis on herd immunity communication 🧪 🔎 10+ years of experimental work 📂 a whole lot of records screened 📊 43 studies (67 effects), >100k participants ⚙️ Three-level model + Publication Bias Tests + MetaForest So… what did we discover? 👀

100

💉 Communicating HI could have different effects: 👉 It could motivate prosocial vaccination (“I’ll vaccinate to protect others”) 👉 But it could also motivate selfish-rational non-vaccination (“I’m safe if others vaccinate”) Which is it? Does communicating HI help vaccination motivation? 🤔

100